基于改进灰狼算法优化长短期记忆网络的光伏功率预测

薛阳; 燕宇铖; 贾巍; 衡雨曦; 张舒翔; 秦瑶

doi:10.19912/j.0254-0096.tynxb.2022-0320

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (7) : 207-213. DOI: 10.19912/j.0254-0096.tynxb.2022-0320

基于改进灰狼算法优化长短期记忆网络的光伏功率预测

薛阳¹, 燕宇铖¹, 贾巍², 衡雨曦¹, 张舒翔¹, 秦瑶¹

作者信息 +

PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM

Xue Yang¹, Yan Yucheng¹, Jia Wei², Heng Yuxi¹, Zhang Shuxiang¹, Qin Yao¹

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文章历史 +

摘要

为提高光伏发电功率预测的准确性,提出一种基于改进自适应因子与精英反向学习策略的改进灰狼算法（IGWO）,用以优化长短期记忆网络（LSTM）预测模型。利用IGWO优化LSTM全连接层参数,建立IGWO-LSTM组合模型预测光伏功率,具有较好的收敛速度与求解效率,也可有效避免局部最优解。最后基于常州某光伏发电站实时数据进行仿真,实验结果表明IGWO-LSTM相对于LSTM光伏功率预测更具准确性。

Abstract

Improving the accuracy of PV power prediction is important for improving the operational efficiency of PV power plants and ensuring the safety and stability of grid-connected PV operation. Therefore, an improved gray wolf algorithm （IGWO） based on improved adaptive factor and elite backward learning strategy is proposed to optimize the long short-term memory network （LSTM） prediction model. The IGWO is used to optimize the LSTM fully connected layer parameters and build a combined IGWO-LSTM model to predict PV power, which has better convergence speed and solution efficiency, and also can effectively avoid local optimal solutions. Finally, based on the simulation of real-time data from a PV power station in Changzhou, the experimental results show that the IGWO-LSTM has more accuracy than the LSTM PV power prediction.

导出引用

薛阳, 燕宇铖, 贾巍, 衡雨曦, 张舒翔, 秦瑶. 基于改进灰狼算法优化长短期记忆网络的光伏功率预测[J]. 太阳能学报. 2023, 44(7): 207-213 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0320

Xue Yang, Yan Yucheng, Jia Wei, Heng Yuxi, Zhang Shuxiang, Qin Yao. PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 207-213 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0320

中图分类号： TM615

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